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仅行磁共振脑放疗:利用扩张卷积神经网络生成的合成 CT 的剂量学评估。

MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network.

机构信息

Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.

Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.

出版信息

Int J Radiat Oncol Biol Phys. 2018 Nov 15;102(4):801-812. doi: 10.1016/j.ijrobp.2018.05.058. Epub 2018 Jun 4.

Abstract

PURPOSE

This work aims to facilitate a fast magnetic resonance (MR)-only workflow for radiation therapy of intracranial tumors. Here, we evaluate whether synthetic computed tomography (sCT) images generated with a dilated convolutional neural network (CNN) enable accurate MR-based dose calculations in the brain.

METHODS AND MATERIALS

We conducted a retrospective study of 52 patients with brain tumors who underwent both computed tomography (CT) and MR imaging for radiation therapy treatment planning. To generate the sCTs, a T1-weighted gradient echo MR sequence was selected from the clinical protocol for multiple types of brain tumors. sCTs were created for all 52 patients with a dilated CNN using 2-fold cross validation; in each fold, 26 patients were used for training and the remaining 26 patients were used for evaluation. For each patient, the clinical CT-based treatment plan was recalculated on sCT. We calculated dose differences and gamma pass rates between CT- and sCT-based plans inside body and planning target volume. Geometric fidelity of the sCT and differences in beam depth and equivalent path length were assessed between both treatment plans.

RESULTS

sCT generation took 1 minute per patient. Over the patient population, the mean absolute error of the sCT within the intersection of body contours was 67 ± 11 HU (±1 standard deviation [SD], range: 51-117 HU), and the mean error was 13 ± 9 HU (±1 SD, range: -2 to 38 HU). Dosimetric analysis showed mean deviations of 0.00% ± 0.02% (±1 SD, range: -0.05 to 0.03) for dose within the body contours and -0.13% ± 0.39% (±1 SD, range: -1.43 to 0.80) inside the planning target volume. Mean γ was 98.8% ± 2.2% for doses >50% of the prescribed dose.

CONCLUSIONS

The presented dilated CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate and can therefore be used for MR-only intracranial radiation therapy treatment planning.

摘要

目的

本研究旨在为颅内肿瘤的放射治疗开发一种快速的磁共振(MR)成像工作流程。本研究旨在评估使用扩张卷积神经网络(CNN)生成的合成计算机断层扫描(sCT)图像是否能够实现基于 MR 的大脑准确剂量计算。

方法和材料

本研究回顾性分析了 52 例接受 CT 和 MR 成像的脑肿瘤患者,这些患者均进行了放射治疗计划。为了生成 sCT,从多种脑肿瘤的临床协议中选择 T1 加权梯度回波 MR 序列。使用 2 倍交叉验证对所有 52 例患者的 sCT 进行了扩张 CNN 生成;在每个折叠中,使用 26 例患者进行训练,其余 26 例患者进行评估。对于每个患者,根据临床 CT 治疗计划对 sCT 进行重新计算。我们计算了基于 CT 和 sCT 的计划在体部和计划靶区内部的剂量差异和伽马通过率。评估了基于两种治疗计划的 sCT 的几何保真度和束深度和等效路径长度的差异。

结果

每个患者的 sCT 生成时间为 1 分钟。在整个患者人群中,体部轮廓交叉处 sCT 的平均绝对误差为 67±11 HU(±1 个标准差[SD],范围:51-117 HU),平均误差为 13±9 HU(±1 SD,范围:-2 至 38 HU)。剂量学分析显示,体部轮廓内的剂量偏差为 0.00%±0.02%(±1 SD,范围:-0.05 至 0.03),计划靶区内部的剂量偏差为-0.13%±0.39%(±1 SD,范围:-1.43 至 0.80)。>50%处方剂量的剂量的平均γ值为 98.8%±2.2%。

结论

所提出的扩张 CNN 从常规 MR 图像中生成 sCT,而不会增加采集时间。剂量学评估表明,在 sCT 上进行的剂量计算是准确的,因此可以用于基于 MR 的颅内放射治疗计划。

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